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ICASSP
2009
IEEE

Microarray classification using block diagonal linear discriminant analysis with embedded feature selection

10 years 5 months ago
Microarray classification using block diagonal linear discriminant analysis with embedded feature selection
In this paper, block diagonal linear discriminant analysis (BDLDA) is improved and applied to gene expression data. BDLDA is a classification tool with embedded feature selection, that has demonstrated good performance on simulated data. However, by using cross validation in training, BDLDA is time consuming, thus not an appropriate algorithm for gene expression data, which has a large number of features and relatively small number of samples. In our algorithm, estimated error rate is used as a measure to choose the best model. The algorithm is optimized by repeating the model construction procedure with previously selected features removed, which leads to increased classification robustness. Our algorithm is tested using 10 fold cross validation. In most simulated and real data, our method outperforms the state-of-the-art techniques, showing promise for its use in microarray classification problems. The resulting block structure allows to identify discriminating correlated genes, ...
Lingyan Sheng, Roger Pique-Regi, Shahab Asgharzade
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Lingyan Sheng, Roger Pique-Regi, Shahab Asgharzadeh, Antonio Ortega
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